Salīdzināt metodes

	Daļēji uzraudzīts Random Forest	Iezīmju izplatīšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	2002
Autors	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Zhu, X. & Ghahramani, Z.
Tips	Semi-supervised ensemble classifier	Graph-based semi-supervised classification
Pirmavots	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Citi nosaukumi	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Saistītās	3	3
Kopsavilkums	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.